The application of deep learning in wireless communication systems has been a research hot spot in recent years and successful deployment of neural networks (NNs) in CSI feedback [1]}, end-to-end communication [2]}, and channel estimation [3]} has been realized. Benefiting from considerable progresses of NN in computer vision and wireless communication [4]}, a convolutional NN-based approach, namely CsiNet, has been proposed in [1]}, exhibiting superior performance in CSI compression by treating the angular-delay domain channel matrix as a sparse 2D image. Particularizing the design of CsiNet in various scenarios, e.g., with high mobility and temporal correlation, a series of works then developed methods using NN for the m-MIMO CSI feedback. Existing works are mainly classified into two categories. One category uses more sophisticated network architectures, including the joint convolutional residual JC-ResNet [6]}, CsiNet+ [7]} and multi-resolution CRNet [8]}. The other category is to exploit extra correlation information. Recurrent NN architectures were employed in [9]} and [10]} to exploit the temporal correlation of CSI in time-varing channels. In [11]}, the uplink channel was used as an auxiliary input of the NN to assist the reconstruction of compressed downlink CSI, which assumes reciprocity between the uplink and downlink channels. In [12]}, a cooperative recovery network, named CoCsiNet, was proposed to cut back on the feedback overhead by exploiting the shared information of UEs in proximity. A new module, named Anci-block, was devised in [13]} by exploiting the visualized characteristics of the angular-delay domain channel matrix.
